The moments of a contour are defined in the same way but computed using the Green’s formula (see http://en.wikipedia.org/wiki/Green_theorem). So, due to a limited raster resolution, the moments computed for a contour are slightly different from the moments computed for the same rasterized contour.

Note

Since the contour moments are computed using Green formula, you may get seemingly odd results for contours with self-intersections, e.g. a zero area (m00) for butterfly-shaped contours.

These values are proved to be invariants to the image scale, rotation, and reflection except the seventh one, whose sign is changed by reflection. This invariance is proved with the assumption of infinite image resolution. In case of raster images, the computed Hu invariants for the original and transformed images are a bit different.

image – Source, an 8-bit single-channel image. Non-zero pixels are treated as 1’s. Zero pixels remain 0’s, so the image is treated as binary . You can use compare() , inRange() , threshold() , adaptiveThreshold() , Canny() , and others to create a binary image out of a grayscale or color one. The function modifies the image while extracting the contours. If mode equals to CV_RETR_CCOMP or CV_RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1).

contours – Detected contours. Each contour is stored as a vector of points.

hierarchy – Optional output vector, containing information about the image topology. It has as many elements as the number of contours. For each i-th contour contours[i] , the elements hierarchy[i][0] , hiearchy[i][1] , hiearchy[i][2] , and hiearchy[i][3] are set to 0-based indices in contours of the next and previous contours at the same hierarchical level, the first child contour and the parent contour, respectively. If for the contour i there are no next, previous, parent, or nested contours, the corresponding elements of hierarchy[i] will be negative.

mode –

Contour retrieval mode (if you use Python see also a note below).

CV_RETR_EXTERNAL retrieves only the extreme outer contours. It sets hierarchy[i][2]=hierarchy[i][3]=-1 for all the contours.

CV_RETR_LIST retrieves all of the contours without establishing any hierarchical relationships.

CV_RETR_CCOMP retrieves all of the contours and organizes them into a two-level hierarchy. At the top level, there are external boundaries of the components. At the second level, there are boundaries of the holes. If there is another contour inside a hole of a connected component, it is still put at the top level.

CV_RETR_TREE retrieves all of the contours and reconstructs a full hierarchy of nested contours. This full hierarchy is built and shown in the OpenCV contours.c demo.

method –

Contour approximation method (if you use Python see also a note below).

CV_CHAIN_APPROX_NONE stores absolutely all the contour points. That is, any 2 subsequent points (x1,y1) and (x2,y2) of the contour will be either horizontal, vertical or diagonal neighbors, that is, max(abs(x1-x2),abs(y2-y1))==1.

CV_CHAIN_APPROX_SIMPLE compresses horizontal, vertical, and diagonal segments and leaves only their end points. For example, an up-right rectangular contour is encoded with 4 points.

CV_CHAIN_APPROX_TC89_L1,CV_CHAIN_APPROX_TC89_KCOS applies one of the flavors of the Teh-Chin chain approximation algorithm. See [TehChin89] for details.

offset – Optional offset by which every contour point is shifted. This is useful if the contours are extracted from the image ROI and then they should be analyzed in the whole image context.

The function retrieves contours from the binary image using the algorithm
[Suzuki85]. The contours are a useful tool for shape analysis and object detection and recognition. See squares.c in the OpenCV sample directory.

Note

Source image is modified by this function. Also, the function does not take into account 1-pixel border of the image (it’s filled with 0’s and used for neighbor analysis in the algorithm), therefore the contours touching the image border will be clipped.

Note

If you use the new Python interface then the CV_ prefix has to be omitted in contour retrieval mode and contour approximation method parameters (for example, use cv2.RETR_LIST and cv2.CHAIN_APPROX_NONE parameters). If you use the old Python interface then these parameters have the CV_ prefix (for example, use cv.CV_RETR_LIST and cv.CV_CHAIN_APPROX_NONE).

Note

An example using the findContour functionality can be found at opencv_source_code/samples/cpp/contours2.cpp

An example using findContours to clean up a background segmentation result at opencv_source_code/samples/cpp/segment_objects.cpp

(Python) An example using the findContour functionality can be found at opencv_source/samples/python2/contours.py

(Python) An example of detecting squares in an image can be found at opencv_source/samples/python2/squares.py

hierarchy – Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).

maxLevel – Maximal level for drawn contours. If it is 0, only
the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.

The function draws contour outlines in the image if
or fills the area bounded by the contours if
. The example below shows how to retrieve connected components from the binary image and label them:

#include "cv.h"#include "highgui.h"usingnamespacecv;intmain(intargc,char**argv){Matsrc;// the first command-line parameter must be a filename of the binary// (black-n-white) imageif(argc!=2||!(src=imread(argv[1],0)).data)return-1;Matdst=Mat::zeros(src.rows,src.cols,CV_8UC3);src=src>1;namedWindow("Source",1);imshow("Source",src);vector<vector<Point>>contours;vector<Vec4i>hierarchy;findContours(src,contours,hierarchy,CV_RETR_CCOMP,CV_CHAIN_APPROX_SIMPLE);// iterate through all the top-level contours,// draw each connected component with its own random colorintidx=0;for(;idx>=0;idx=hierarchy[idx][0]){Scalarcolor(rand()&255,rand()&255,rand()&255);drawContours(dst,contours,idx,color,CV_FILLED,8,hierarchy);}namedWindow("Components",1);imshow("Components",dst);waitKey(0);}

Note

An example using the drawContour functionality can be found at opencv_source_code/samples/cpp/contours2.cpp

An example using drawContours to clean up a background segmentation result at opencv_source_code/samples/cpp/segment_objects.cpp

(Python) An example using the drawContour functionality can be found at opencv_source/samples/python2/contours.py

approxCurve – Result of the approximation. The type should match the type of the input curve. In case of C interface the approximated curve is stored in the memory storage and pointer to it is returned.

epsilon – Parameter specifying the approximation accuracy. This is the maximum distance between the original curve and its approximation.

closed – If true, the approximated curve is closed (its first and last vertices are connected). Otherwise, it is not closed.

minimal_perimeter – Approximates only those contours whose perimeters are not less than minimal_perimeter . Other chains are removed from the resulting structure.

recursive – Recursion flag. If it is non-zero, the function approximates all chains that can be obtained from chain by using the h_next or v_next links. Otherwise, the single input chain is approximated.

This is a standalone contour approximation routine, not represented in the new interface. When FindContours() retrieves contours as Freeman chains, it calls the function to get approximated contours, represented as polygons.

oriented – Oriented area flag. If it is true, the function returns a signed area value, depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can determine orientation of a contour by taking the sign of an area. By default, the parameter is false, which means that the absolute value is returned.

The function computes a contour area. Similarly to
moments() , the area is computed using the Green formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using
drawContours() or
fillPoly() , can be different.
Also, the function will most certainly give a wrong results for contours with self-intersections.

hull – Output convex hull. It is either an integer vector of indices or vector of points. In the first case, the hull elements are 0-based indices of the convex hull points in the original array (since the set of convex hull points is a subset of the original point set). In the second case, hull elements are the convex hull points themselves.

hull_storage – Output memory storage in the old API (cvConvexHull2 returns a sequence containing the convex hull points or their indices).

clockwise – Orientation flag. If it is true, the output convex hull is oriented clockwise. Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing to the right, and its Y axis pointing upwards.

orientation – Convex hull orientation parameter in the old API, CV_CLOCKWISE or CV_COUNTERCLOCKWISE.

returnPoints – Operation flag. In case of a matrix, when the flag is true, the function returns convex hull points. Otherwise, it returns indices of the convex hull points. When the output array is std::vector, the flag is ignored, and the output depends on the type of the vector: std::vector<int> implies returnPoints=true, std::vector<Point> implies returnPoints=false.

The functions find the convex hull of a 2D point set using the Sklansky’s algorithm
[Sklansky82]
that has
O(N logN) complexity in the current implementation. See the OpenCV sample convexhull.cpp that demonstrates the usage of different function variants.

Note

An example using the convexHull functionality can be found at opencv_source_code/samples/cpp/convexhull.cpp

convexhull – Convex hull obtained using convexHull() that should contain indices of the contour points that make the hull.

convexityDefects – The output vector of convexity defects. In C++ and the new Python/Java interface each convexity defect is represented as 4-element integer vector (a.k.a. cv::Vec4i): (start_index,end_index,farthest_pt_index,fixpt_depth), where indices are 0-based indices in the original contour of the convexity defect beginning, end and the farthest point, and fixpt_depth is fixed-point approximation (with 8 fractional bits) of the distance between the farthest contour point and the hull. That is, to get the floating-point value of the depth will be fixpt_depth/256.0. In C interface convexity defect is represented by CvConvexityDefect structure - see below.

storage – Container for the output sequence of convexity defects. If it is NULL, the contour or hull (in that order) storage is used.

The function finds all convexity defects of the input contour and returns a sequence of the CvConvexityDefect structures, where CvConvexityDetect is defined as:

structCvConvexityDefect{CvPoint*start;// point of the contour where the defect beginsCvPoint*end;// point of the contour where the defect endsCvPoint*depth_point;// the farthest from the convex hull point within the defectfloatdepth;// distance between the farthest point and the convex hull};

The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of all. It returns the rotated rectangle in which the ellipse is inscribed. The algorithm [Fitzgibbon95] is used.
Developer should keep in mind that it is possible that the returned ellipse/rotatedRect data contains negative indices, due to the data points being close to the border of the containing Mat element.

Note

An example using the fitEllipse technique can be found at opencv_source_code/samples/cpp/fitellipse.cpp

line – Output line parameters. In case of 2D fitting, it should be a vector of 4 elements (like Vec4f) - (vx,vy,x0,y0), where (vx,vy) is a normalized vector collinear to the line and (x0,y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like Vec6f) - (vx,vy,vz,x0,y0,z0), where (vx,vy,vz) is a normalized vector collinear to the line and (x0,y0,z0) is a point on the line.

distType – Distance used by the M-estimator (see the discussion below).

param – Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value is chosen.

reps – Sufficient accuracy for the radius (distance between the coordinate origin and the line).

aeps – Sufficient accuracy for the angle. 0.01 would be a good default value for reps and aeps.

The function fitLine fits a line to a 2D or 3D point set by minimizing
where
is a distance between the
point, the line and
is a distance function, one of the following:

distType=CV_DIST_L2

distType=CV_DIST_L1

distType=CV_DIST_L12

distType=CV_DIST_FAIR

distType=CV_DIST_WELSCH

distType=CV_DIST_HUBER

The algorithm is based on the M-estimator (
http://en.wikipedia.org/wiki/M-estimator
) technique that iteratively fits the line using the weighted least-squares algorithm. After each iteration the weights
are adjusted to be inversely proportional to
.

The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a specified point set. See the OpenCV sample minarea.cpp .
Developer should keep in mind that the returned rotatedRect can contain negative indices when data is close the the containing Mat element boundary.

measureDist – If true, the function estimates the signed distance from the point to the nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not.

The function determines whether the
point is inside a contour, outside, or lies on an edge (or coincides
with a vertex). It returns positive (inside), negative (outside), or zero (on an edge) value,
correspondingly. When measureDist=false , the return value
is +1, -1, and 0, respectively. Otherwise, the return value
is a signed distance between the point and the nearest contour
edge.

See below a sample output of the function where each image pixel is tested against the contour.

Andrew W. Fitzgibbon, R.B.Fisher. A Buyer’s Guide to Conic Fitting. Proc.5th British Machine Vision Conference, Birmingham, pp. 513-522, 1995. The technique used for ellipse fitting is the first one described in this summary paper.